Improving mechanical performance in material extrusion parts via optimized toolpath planning

Chapter 6 - Polymer and composites additive manufacturing: material extrusion processes

The sustainability of additive manufacturing (AM) has attracted attention nowadays, with different aspects needed to be considered. A key effort in this direction is the replacement of fossil fuel–originating polymers with biologically derived polymers. To achieve this, biologically derived polymers should exhibit a performance similar to that of commonly used polymers. Bacterially produced pure polyhydroxyalkanoate (PHA) was recently proposed by filament vendors for material extrusion (MEX) AM. Despite extensive research on the utilization of PHA in polymeric blends, the literature on the printability metrics of pure PHA is limited. At the same time, it has a high potential as an alternative to common petrochemical polymers. In this study, the impact of the main generic settings on the mechanical response of pure bio-sourced PHA in MEX AM was investigated. Four critical 3D printing parameters (print speed, layer height, nozzle temperature, and strand width) were evaluated using a robust design to optimize the tensile and impact metrics. Regression modeling was performed, and predictive equations were compiled and verified using validation testing. The quality of the samples was evaluated using electron microscopy. The analysis proved the importance of layer thickness in the tensile score, whereas nozzle temperature was found to be the most influential setting for impact loading. An increase of approximately 20% can be achieved by selecting appropriate 3D printing settings in the tensile test, whereas the impact strength can be radically improved by up to 550%.Graphical

For the last few decades, the rapid growth of Additive Manufacturing (AM) technologies has been seeable. It is expected to keep maturing continuously due to its advantages compared to conventional manufacturing technologies: flexibility, reliability, energy consumption, and material efficiency. This research article addresses the development and production of a stapler using the Material Extrusion AM process. It is intended to show the development steps to redesign an everyday stapler, into an added-value tool, from the selection and fixture of the CAD model and generative design through Fusion 360 to its optimization on nTopology, simulation, and plot of the part in Eiger.

Among all the additive manufacturing (AM) technologies, the material extrusion-based AM is the most popular, and widely used for prototyping, rapid tooling, and direct part production in the current scenario. In material extrusion AM (MEAM), a solid form of material is converted to a semi-molten form and extruded out of a nozzle to deposit in a layer-by-layer manner. Nowadays MEAM three-dimensional (3D) printers are very popular among different application sectors. This motivates us to develop various commercial as well as open-source entry-level 3D printers such as the development of filament extruder 3D printers, pellet extruder 3D printer, or a combination of both. However, to develop or operate these printers, preliminary knowledge of various extrusion parameters is required to obtain the desired quality in the fabricated parts. Therefore, an initial attempt has been made to provide information related to various extrusion parameters such as definition, working range, and the effect of these parameters on performance measured. The impact of process parameters on the performance of either filament extrusion AM or pellet/granules extrusion AM has only been investigated in the studies available in the literature. However, this study provides a comprehensive review of the effects of process parameters on performance measurements for both types of extrusion-based AM processes. In this research work, along with a detailed review of previous works, a study of various process parameters, modeling, and methodology has been provided. The statistical modeling approach, histogram normal plots have been provided along with mean, standard deviation, and percentage to decide the optimum process parameters based on data available in the existing literature.

Digital manufacturing techniques, such as computer numerically controlled (CNC) machining and additive manufacturing (AM), have greatly increased the design space available to engineers with respect to both geometry and materials. Obviously, certain geometries cannot be realized by anything other than a layer-by-layer AM process, and certain materials can only be formed using traditional subtractive approaches, leaving designers with little choice. However, for the wide array of structures that could be realized in a variety of different ways, the choice of manufacturing process is just another faction in the overall design process. Planar lattice structures have complex repeating geometric features which are best designed using computer aided design (CAD) tools, and these structures can easily be manufactured using a variety of different digital manufacturing processes. For this study a set of planar lattice structures were designed and fabricated from acrylonitrile butadiene styrene (ABS) plastic using both a CNC milling machine and a material extrusion AM machine. All test lattices were then loaded in tension to failure to determine their mechanical performance. The determined properties were then compared to analytical predictions from the literature, and an analysis of the impact of a chosen manufacturing process was performed. The results obtained show good agreement with publish models for lattice performance, and additionally suggest that the choice of manufacturing process has a measurable effect on the mechanical performance of the resulting structures for this specific material and geometry combination, but it is important to note that this study is just an example of how to mitigate the uncertainties associated with the freedoms offered by advanced manufacturing processes, and more work in needed in this important area of study.

Polyethylene terephthalate glycol (PETG) and silicon nitride (Si3N4) were combined to create five composite materials with Si3N4 loadings ranging from 2.0 wt.% to 10.0 wt.%. The goal was to improve the mechanical properties of PETG in material extrusion (MEX) additive manufacturing (AM) and assess the effectiveness of Si3N4 as a reinforcing agent for this particular polymer. The process began with the production of filaments, which were subsequently fed into a 3D printer to create various specimens. The specimens were manufactured according to international standards to ensure their suitability for various tests. The thermal, rheological, mechanical, electrical, and morphological properties of the prepared samples were evaluated. The mechanical performance investigations performed included tensile, flexural, Charpy impact, and microhardness tests. Scanning electron microscopy and energy-dispersive X-ray spectroscopy mapping were performed to investigate the structures and morphologies of the samples, respectively. Among all the composites tested, the PETG/6.0 wt.% Si3N4 showed the greatest improvement in mechanical properties (with a 24.5% increase in tensile strength compared to unfilled PETG polymer), indicating its potential for use in MEX 3D printing when enhanced mechanical performance is required from the PETG polymer.

Graphene is emerging as an excellent material choice for high-performance electrical and electrochemical devices such as humidity sensors. The rapid technological evolution in additive manufacturing (AM) enables freedom to design and process multiple materials for scalable fabrication of sensors. Herein, we introduce a hybrid AM approach to fabricate a graphene-based humidity sensor and study its sensing performance. A powder-bed binder-jetting AM technique is used to build a porous 3D-structure of thermally reduced graphene oxide as the humidity sensing element. In parallel, a material extrusion AM approach is used to make a silicone-based hollow cube as the housing of the graphene structure. The AM fabricated sensor shows high sensitivity when tested in the relative humidity (RH) range of 6.4–97.3% with a rapid response time of 7 s at 45% RH due to the open porous structure formed by the binder-jetting approach. Sensing performance was investigated at low and medium RH in the temperature range of 25–80 °C and the device demonstrated a negligible temperature dependence. The presented graphene-based humidity sensor also shows good repeatability in RH measurements.

Material Extrusion (MEX) additive manufacturing offers a versatile platform for producing lightweight, structurally optimized components. This study investigates the simultaneous optimization of topology and infill density using three polymer composite materials, PPA-CF, PAHT-CF, and ABS, selected for their mechanical performance, cost efficiency, and printability. Cylindrical specimens were fabricated with nine mass retention levels (100% to 33%) by systematically varying topology and infill parameters. Compression testing was conducted to assess stiffness, deformation behavior, and structural integrity under simulated operational loads. Results show that combining topology optimization with variable infill density can significantly reduce material usage and manufacturing time while maintaining mechanical reliability across all three materials. PAHT-CF demonstrated the highest strength-to-weight performance, while ABS offered cost-effective alternatives for less demanding applications. The study establishes clear relationships between design strategies and material behavior, enabling the production of net-shape satellite support structures with fewer design iterations and improved throughput. These findings support the adoption of resource-efficient manufacturing practices and provide a framework for sustainable, low- to mid-volume production in high-value manufacturing industries. Overall, the integration of design and material optimization advances the potential of additive manufacturing for scalable, cost-effective, and environmentally conscious aerospace solutions.

Purpose Fibre-reinforced polymers offer enhanced in-plane mechanical properties in material extrusion (MEX) additive manufacturing, expanding its applicability. However, fibres have minimal impact on the mechanical properties in the build direction, limiting the industrial applications of MEX. This study aims to propose a novel method using a customised toolpath to enhance the interlayer mechanical properties of carbon fibre-reinforced nylon 6 (CF-PA6) structures in an easily accessible manner, requiring no hardware modifications. Design/methodology/approach The customised toolpath discretises the continuous bead into sub-sections and implements periodic vertical toolhead movement during fabrication. Three bead width configurations of MEX-fabricated CF-PA6 specimens were investigated using microscopy, thermal analysis and mechanical performance assessment. Findings In all configurations, the customised toolpath outperformed the conventional toolpath, showing significant increases in ultimate tensile strength (238%–513%), failure strain (70%–349%) and modulus (71%–164%). The main factors contributing to these improvements were found to be redistribution of fibre orientation and thermal effects. In addition, the enhancements became more pronounced as the print width increased. Originality/value This novel fabrication method improves the interlayer mechanical properties of CF-PA6 through software modifications alone. To the best of the authors’ knowledge, this approach has not been demonstrated on fibre-reinforced materials and may enable printing of high-performance polymers on less capable MEX printers.

A physical hash for preventing and detecting cyber-physical attacks in additive manufacturing systems

Purpose Regardless of the materials used, additive manufacturing (AM) is one of the most popular emerging fabrication processes used for creating complex and intricate structural components. This study aims to investigate the effects of process parameters – namely, nozzle diameter, layer thickness and infill density on microstructure as well as the mechanical properties of 17–4 PH stainless steel specimens fabricated via material extrusion AM. Design/methodology/approach The experimental approach investigates the effects of printing parameters, including nozzle diameter, layer thickness and infill density, on surface roughness, physical and mechanical properties of the printed specimens. The tests were triplicated to ensure reproducibility of the experimental results. Findings The highest ultimate tensile strength, 795.26 MPa, was obtained on specimen that was fabricated with a 0.4 mm nozzle diameter, 0.14 mm layer thickness and 30% infill density. Furthermore, a 0.4 mm nozzle diameter also provided slightly better ductility. This came at the expense of surface finishing, as a 0.25 mm nozzle diameter exhibited better surface finishing over a 0.4 mm nozzle diameter. Infill density was shown to slightly influence the tensile properties, whereas layer thickness showed a significant effect on surface roughness. By contrast, hardness and ductility were independent of nozzle diameter, layer thickness and infill density. Originality/value This paper presents a comprehensive analysis relating to various input printing parameters on microstructural, physical and mechanical properties of additively manufactured 17–4 PH stainless steel to improve the printability and processability via AM.

Fused Filament Fabrication (FFF) is a widely embraced material extrusion (MEX) additive manufacturing (AM) process to produce complex three-dimensional structures, and it is typically used in the fabrication of biodegradable polymers for biomedical applications. However, FFF as a fabrication process for blended polymeric materials needs to be optimized for enhanced mechanical properties. In this work, biodegradable polylactic acid (PLA)/polyhydroxyalkanoate (PHA) dog-bone and notched specimens are printed to determine optimum printing parameters for superior mechanical properties in FFF additive manufacturing. The effect of layer thickness, infill density, and printing bed temperature on mechanical properties is investigated by employing a design of experiments (DoE) approach using response surface methodology (RSM). Experimental results showed the significance of the opted parameters for mechanical properties of the PLA/PHA blend. Then, optimum values for layer thickness, infill density, and printing bed temperature are identified for tensile and impact strength and an empirical relationship between parameters is formulated for low density and cost-effective fabrication. Finally, the analysis of variance (ANOVA) is performed to check the adequacy of the model for the influence of process parameters and their mutual interactions. The verification experiments validated the adequacy of the proposed model for PLA/PHA blend in FFF additive manufacturing.

Acrylonitrile styrene acrylate (ASA) is increasingly popular among traditional engineering polymers for material extrusion (MEX) additive manufacturing (AM). Satisfactory printability and engineering metrics and excellent stability in hard environmental conditions explain this status. To achieve the highest possible performance levels, the optimization of the generic printing control settings is critical. This paper focuses on delivering enhanced mechanical and morphological characteristics when printing with ASA. To meet these goals, an L25 orthogonal robust design was compiled and executed, enabling the simultaneous assessment of six (6) control parameters, i.e., the strand width (SW), infill orientation (angle) (AINF), layer thickness (TL), printing speed (PS), nozzle temperature (TN), and bed temperature (TB). A massive experimental course with two hundred and ninety specimens was engaged to deliver response metrics, such as tensile ultimate strength (σBT), tensile yield strength (σBY), tensile modulus of elasticity (ET), flexural strength (σBF), and flexural modulus of elasticity (EF). Three modeling methods were applied to compare their efficacy in these loading scenarios: the Reduced Quadratic Regression Model (RQRM), Linear Regression Model (LRM), and Quadratic Regression Model (QRM). RQRM and QRM achieved similar prediction accuracies, whereas LRM yielded poorer predictive results than the other methods. Confirmation runs validated the validity of the compiled prediction functions, thereby making them suitable for direct industrial use. The most influential parameter was the AINF. The optimized 3D printing settings improved all mechanical performance metrics by more than 40%, while the flexural strength was improved by more than 60%.

Designing efficient closed-loop control algorithms is a key issue in Additive Manufacturing (AM), as various aspects of the AM process require continuous monitoring and regulation, with temperature being a particularly significant factor. Here we study closed-loop control for the temperatures in the extruder of a Material Extrusion AM system, specifically a Big Area Additive Manufacturing (BAAM) system. Previous approaches for temperature control in AM either require the knowledge of exact model parameters, or involve discretisation of the state and action spaces to employ traditional data-driven control techniques. On the other hand, modern algorithms that can handle continuous state and action space problems require a large number of hyperparameter tuning to ensure good performance. In this work, we circumvent the above limitations by making use of a state space temperature model while focusing on both model-based and data-driven methods. We adopt the Linear Quadratic Tracking (LQT) framework and utilise the quadratic structure of the value function in the model-based analytical solution to produce a data-driven approximation formula for the optimal controller. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of a BAAM system and perform an in-depth comparison of the performance of these methods. We find that we can learn an effective controller using solely simulated input–output process data. Our approach achieves parity in performance compared to model-based controllers and so lessens the need for estimating a large number of parameters of the often intricate and complicated process model. We believe this result is an important step towards achieving autonomous intelligent manufacturing.

This work presents a decision-making methodology that allows the merging of quantitative and qualitative decision variables for selecting the optimal metal Additive Manufacturing (AM) technology. The approach is applied on two competing technologies in the field of metal AM industry, i.e., the metal extrusion AM process (metal FFF) and the Laser Powder Bed Fusion process (LPBF) with single and multiple lasers, which represent the benchmark solution currently on the market. A comprehensive techno-economical comparison is presented where the two processes are analysed in terms of process capabilities (quality, easiness of use, setup time, range of possible materials, etc.) and costs, considering two different production scenarios and different parts’ geometries. In the first scenario, the AM system is assumed to be dedicated to one single part production while in this second scenario, the AM system is assumed to be saturated, as devoted to producing a wide mix of part types. For each scenario, two different part types made of 17–4 PH stainless steel are considered as a reference to investigate the effect of shape complexity, part size and production times to select the best technology when metal FFF and LPBF must be considered. The first part type refers to an extrusion die, to represent typical shapes of interest in the tooling industry, while the second part type is an impeller which can be used in many different industrial sectors, ranging from oil and gas to aerospace. In order to include quantitative and qualitative criteria, a decision-making model based on Analytic Hierarchy Process (AHP) is proposed as the enabler tool for decision making. The proposed approach allows to determine the most effective solution depending on the different production configurations and part types and can be used as a guideline and extended to include other technologies in the field of metal AM. On the other side, the critical discussion of the criteria selected, and the results achieved allow to highlight the pros and cons of the competing technologies, thus defining the existing limits to define directions for future research.